1. Field of the Invention
The disclosed methods and systems relate generally to optimization, and more particularly to the optimization of innovative capacity, which is an economy's potential, at a given point in time, for producing a stream of commercially relevant innovations, and the economic value of knowledge, human and risk capital asset sets and more particularly to a method of performing an analysis of policy operations that optimize innovation and commercialization performance for large aggregates of enterprises contributing to the knowledge based economy of nations, states, provinces and industry sectors.
2. Background Information
There are a number of teachings in the prior art describing business systems within individual enterprises and firms that include: (a) methods for performing portfolio analysis using quantified data for making investment decisions and for optimizing the resource budget as taught by Huettl et al. in US Patent Application Publication No. U.S. 2004/0024673 published on Feb. 5, 2004; (b) methods and systems for optimization of economic value from asset sets for manufacturing processes within the firm by Martin in U.S. Patent Application Publication No. 2005/0234815 published on Oct. 20, 2005; and (c) enterprise information evolution analysis systems and methods as taught by Hatcher et al. in U.S. Patent Application Publication No. 2004/0093244 published on May 13, 2004. These prior art teachings do not incorporate systems comprised of aggregates of several firms or collections of both private and public assets in industry sectors, provincial, state or national economies with a focus on innovation, commercialization and the knowledge based economy.
Traditionally, there has been no effective way to optimize the economic performance of the knowledge, human and risk capital asset set or sets contained within the innovation and commercialization process so that maximum innovative capacity and economic value is achieved from each of the assets in the set or from the combination of assets. There has been no accepted common method, or process for first determining the optimal balance among sets of assets that include public research and development (called public R&D herein), private research and development (called private R&D herein), highly qualified personnel (called HQP herein)—that may be further categorized as HQP researchers and HQP management—and risk venture capital; and second, the set of policy operations that best optimize innovative capacity and economic value for the knowledge based economy that is primarily driven by investments in these assets. The common approach to this problem has been to report independent data and indicators for various types of assets that are deemed to influence innovation, commercialization and economic value but not to report quantitative relationships among these assets. There are several shortcomings of this approach.
First, in the absence of defined relationships among assets there have been few means of determining either the individual contribution of each asset or the collective contribution of the assets to innovative capacity or economic value.
Second, effective economic modeling to the stage and sub-stage level has been very difficult to accomplish because of the inherent complexity of the innovation and commercialization process and its sub-processes and the absence of common definitions for stages and sub-stages within the process and sub-processes. The lack of sufficient knowledge related to asset inter-relationships has limited their quantification and the development of accepted models for innovative capacity and economic value. There has also been limited success in decomposing the asset base to the stage, sub-stage and even more detailed levels, as shown in layer 1 of FIG. 1 and then to try to model and fairly attribute value at each stage or sub-stage. Existing approaches have not been able to address complex asset combinations at the process stage level. This has severely limited the availability of such metrics.
Third, combining sub-process asset models into process models for larger asset sets is also extremely complex and there has been insufficient knowledge of input and output indicators related to innovative capacity to do so.
Traditionally in innovation and commercialization processes, most asset investment data have been reported annually. Since progress in developing economic value along the stages in the innovation and commercialization process may occur over long time periods of many years and often more than a decade, the impact of a specific asset or of a specific policy operation within the overall process on economic value at any particular time has been difficult to discern from the current reporting system. Thus, there is a need to determine how economic value is affected by changes in policy operations at the progressive stages and sub-stages in the innovation supply chain continuously over time at progressively more and more detailed levels of approximation.